Overview

Dataset statistics

Number of variables17
Number of observations1003
Missing cells149
Missing cells (%)0.9%
Duplicate rows3
Duplicate rows (%)0.3%
Total size in memory627.8 KiB
Average record size in memory640.9 B

Variable types

Text1
Categorical7
Numeric7
DateTime2

Alerts

gross margin percentage has constant value "4.761904762"Constant
Dataset has 3 (0.3%) duplicate rowsDuplicates
Branch is highly overall correlated with CityHigh correlation
City is highly overall correlated with BranchHigh correlation
Quantity is highly overall correlated with Tax 5% and 3 other fieldsHigh correlation
Tax 5% is highly overall correlated with Quantity and 4 other fieldsHigh correlation
Total is highly overall correlated with Quantity and 4 other fieldsHigh correlation
Unit price is highly overall correlated with Tax 5% and 3 other fieldsHigh correlation
cogs is highly overall correlated with Quantity and 4 other fieldsHigh correlation
gross income is highly overall correlated with Quantity and 4 other fieldsHigh correlation
Customer type has 79 (7.9%) missing valuesMissing
Product line has 43 (4.3%) missing valuesMissing
Quantity has 20 (2.0%) missing valuesMissing

Reproduction

Analysis started2026-01-19 11:49:01.906134
Analysis finished2026-01-19 11:49:08.021602
Duration6.12 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Distinct1000
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
2026-01-19T12:49:08.198041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters11033
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique997 ?
Unique (%)99.4%

Sample

1st row750-67-8428
2nd row226-31-3081
3rd row631-41-3108
4th row123-19-1176
5th row373-73-7910
ValueCountFrequency (%)
849-09-38072
 
0.2%
745-74-07152
 
0.2%
452-04-88082
 
0.2%
433-75-69871
 
0.1%
252-56-26991
 
0.1%
871-79-84831
 
0.1%
848-62-72431
 
0.1%
631-41-31081
 
0.1%
123-19-11761
 
0.1%
373-73-79101
 
0.1%
Other values (990)990
98.7%
2026-01-19T12:49:08.522098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
-2006
18.2%
2958
8.7%
6954
8.6%
1951
8.6%
8949
8.6%
5930
8.4%
4923
8.4%
3910
8.2%
7899
8.1%
0814
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)11033
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
-2006
18.2%
2958
8.7%
6954
8.6%
1951
8.6%
8949
8.6%
5930
8.4%
4923
8.4%
3910
8.2%
7899
8.1%
0814
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11033
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
-2006
18.2%
2958
8.7%
6954
8.6%
1951
8.6%
8949
8.6%
5930
8.4%
4923
8.4%
3910
8.2%
7899
8.1%
0814
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11033
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
-2006
18.2%
2958
8.7%
6954
8.6%
1951
8.6%
8949
8.6%
5930
8.4%
4923
8.4%
3910
8.2%
7899
8.1%
0814
7.4%

Branch
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
A
342 
B
333 
C
328 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1003
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowC
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A342
34.1%
B333
33.2%
C328
32.7%

Length

2026-01-19T12:49:08.643712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-19T12:49:08.768049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
a342
34.1%
b333
33.2%
c328
32.7%

Most occurring characters

ValueCountFrequency (%)
A342
34.1%
B333
33.2%
C328
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1003
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A342
34.1%
B333
33.2%
C328
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1003
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A342
34.1%
B333
33.2%
C328
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1003
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A342
34.1%
B333
33.2%
C328
32.7%

City
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size63.4 KiB
Yangon
342 
Mandalay
333 
Naypyitaw
328 

Length

Max length9
Median length8
Mean length7.6450648
Min length6

Characters and Unicode

Total characters7668
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYangon
2nd rowNaypyitaw
3rd rowYangon
4th rowYangon
5th rowYangon

Common Values

ValueCountFrequency (%)
Yangon342
34.1%
Mandalay333
33.2%
Naypyitaw328
32.7%

Length

2026-01-19T12:49:08.865895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-19T12:49:08.976358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
yangon342
34.1%
mandalay333
33.2%
naypyitaw328
32.7%

Most occurring characters

ValueCountFrequency (%)
a1997
26.0%
n1017
13.3%
y989
12.9%
Y342
 
4.5%
g342
 
4.5%
o342
 
4.5%
M333
 
4.3%
d333
 
4.3%
l333
 
4.3%
N328
 
4.3%
Other values (4)1312
17.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)7668
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1997
26.0%
n1017
13.3%
y989
12.9%
Y342
 
4.5%
g342
 
4.5%
o342
 
4.5%
M333
 
4.3%
d333
 
4.3%
l333
 
4.3%
N328
 
4.3%
Other values (4)1312
17.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7668
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1997
26.0%
n1017
13.3%
y989
12.9%
Y342
 
4.5%
g342
 
4.5%
o342
 
4.5%
M333
 
4.3%
d333
 
4.3%
l333
 
4.3%
N328
 
4.3%
Other values (4)1312
17.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7668
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1997
26.0%
n1017
13.3%
y989
12.9%
Y342
 
4.5%
g342
 
4.5%
o342
 
4.5%
M333
 
4.3%
d333
 
4.3%
l333
 
4.3%
N328
 
4.3%
Other values (4)1312
17.1%

Customer type
Categorical

Missing 

Distinct2
Distinct (%)0.2%
Missing79
Missing (%)7.9%
Memory size61.9 KiB
Normal
470 
Member
454 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters5544
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMember
2nd rowNormal
3rd rowNormal
4th rowMember
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal470
46.9%
Member454
45.3%
(Missing)79
 
7.9%

Length

2026-01-19T12:49:09.073286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-19T12:49:09.173963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
normal470
50.9%
member454
49.1%

Most occurring characters

ValueCountFrequency (%)
r924
16.7%
m924
16.7%
e908
16.4%
N470
8.5%
o470
8.5%
a470
8.5%
l470
8.5%
M454
8.2%
b454
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)5544
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r924
16.7%
m924
16.7%
e908
16.4%
N470
8.5%
o470
8.5%
a470
8.5%
l470
8.5%
M454
8.2%
b454
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5544
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r924
16.7%
m924
16.7%
e908
16.4%
N470
8.5%
o470
8.5%
a470
8.5%
l470
8.5%
M454
8.2%
b454
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5544
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r924
16.7%
m924
16.7%
e908
16.4%
N470
8.5%
o470
8.5%
a470
8.5%
l470
8.5%
M454
8.2%
b454
8.2%

Gender
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size60.9 KiB
Female
502 
Male
501 

Length

Max length6
Median length6
Mean length5.000997
Min length4

Characters and Unicode

Total characters5016
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Female502
50.0%
Male501
50.0%

Length

2026-01-19T12:49:09.267873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-19T12:49:09.377913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
female502
50.0%
male501
50.0%

Most occurring characters

ValueCountFrequency (%)
e1505
30.0%
a1003
20.0%
l1003
20.0%
F502
 
10.0%
m502
 
10.0%
M501
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)5016
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1505
30.0%
a1003
20.0%
l1003
20.0%
F502
 
10.0%
m502
 
10.0%
M501
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5016
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1505
30.0%
a1003
20.0%
l1003
20.0%
F502
 
10.0%
m502
 
10.0%
M501
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5016
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1505
30.0%
a1003
20.0%
l1003
20.0%
F502
 
10.0%
m502
 
10.0%
M501
 
10.0%

Product line
Categorical

Missing 

Distinct6
Distinct (%)0.6%
Missing43
Missing (%)4.3%
Memory size73.6 KiB
Fashion accessories
172 
Electronic accessories
165 
Food and beverages
165 
Sports and travel
163 
Home and lifestyle
151 

Length

Max length22
Median length19
Mean length18.546875
Min length17

Characters and Unicode

Total characters17805
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHealth and beauty
2nd rowElectronic accessories
3rd rowHome and lifestyle
4th rowHealth and beauty
5th rowSports and travel

Common Values

ValueCountFrequency (%)
Fashion accessories172
17.1%
Electronic accessories165
16.5%
Food and beverages165
16.5%
Sports and travel163
16.3%
Home and lifestyle151
15.1%
Health and beauty144
14.4%
(Missing)43
 
4.3%

Length

2026-01-19T12:49:09.465959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-19T12:49:09.585169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
and623
24.5%
accessories337
13.3%
fashion172
 
6.8%
electronic165
 
6.5%
food165
 
6.5%
beverages165
 
6.5%
sports163
 
6.4%
travel163
 
6.4%
home151
 
5.9%
lifestyle151
 
5.9%
Other values (2)288
11.3%

Most occurring characters

ValueCountFrequency (%)
e2238
12.6%
a1748
 
9.8%
s1662
 
9.3%
1583
 
8.9%
o1318
 
7.4%
c1004
 
5.6%
r993
 
5.6%
n960
 
5.4%
t930
 
5.2%
i825
 
4.6%
Other values (15)4544
25.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)17805
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e2238
12.6%
a1748
 
9.8%
s1662
 
9.3%
1583
 
8.9%
o1318
 
7.4%
c1004
 
5.6%
r993
 
5.6%
n960
 
5.4%
t930
 
5.2%
i825
 
4.6%
Other values (15)4544
25.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)17805
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e2238
12.6%
a1748
 
9.8%
s1662
 
9.3%
1583
 
8.9%
o1318
 
7.4%
c1004
 
5.6%
r993
 
5.6%
n960
 
5.4%
t930
 
5.2%
i825
 
4.6%
Other values (15)4544
25.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)17805
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e2238
12.6%
a1748
 
9.8%
s1662
 
9.3%
1583
 
8.9%
o1318
 
7.4%
c1004
 
5.6%
r993
 
5.6%
n960
 
5.4%
t930
 
5.2%
i825
 
4.6%
Other values (15)4544
25.5%

Unit price
Real number (ℝ)

High correlation 

Distinct938
Distinct (%)94.2%
Missing7
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean55.764568
Minimum10.08
Maximum99.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2026-01-19T12:49:09.722756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10.08
5-th percentile15.275
Q133.125
median55.42
Q378.085
95-th percentile97.2125
Maximum99.96
Range89.88
Interquartile range (IQR)44.96

Descriptive statistics

Standard deviation26.510165
Coefficient of variation (CV)0.47539443
Kurtosis-1.2226701
Mean55.764568
Median Absolute Deviation (MAD)22.575
Skewness0.00017534848
Sum55541.51
Variance702.78887
MonotonicityNot monotonic
2026-01-19T12:49:09.846301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83.773
 
0.3%
88.342
 
0.2%
60.32
 
0.2%
32.322
 
0.2%
32.252
 
0.2%
99.962
 
0.2%
45.582
 
0.2%
39.752
 
0.2%
68.712
 
0.2%
45.382
 
0.2%
Other values (928)975
97.2%
(Missing)7
 
0.7%
ValueCountFrequency (%)
10.081
0.1%
10.131
0.1%
10.161
0.1%
10.171
0.1%
10.181
0.1%
10.531
0.1%
10.561
0.1%
10.591
0.1%
10.691
0.1%
10.751
0.1%
ValueCountFrequency (%)
99.962
0.2%
99.921
0.1%
99.891
0.1%
99.831
0.1%
99.822
0.2%
99.791
0.1%
99.781
0.1%
99.731
0.1%
99.711
0.1%
99.71
0.1%

Quantity
Real number (ℝ)

High correlation  Missing 

Distinct10
Distinct (%)1.0%
Missing20
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean5.5015259
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2026-01-19T12:49:09.955489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9246734
Coefficient of variation (CV)0.53161131
Kurtosis-1.216926
Mean5.5015259
Median Absolute Deviation (MAD)2
Skewness0.016679454
Sum5408
Variance8.5537146
MonotonicityNot monotonic
2026-01-19T12:49:10.046600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10116
11.6%
1111
11.1%
4108
10.8%
7100
10.0%
5100
10.0%
696
9.6%
992
9.2%
289
8.9%
389
8.9%
882
8.2%
(Missing)20
 
2.0%
ValueCountFrequency (%)
1111
11.1%
289
8.9%
389
8.9%
4108
10.8%
5100
10.0%
696
9.6%
7100
10.0%
882
8.2%
992
9.2%
10116
11.6%
ValueCountFrequency (%)
10116
11.6%
992
9.2%
882
8.2%
7100
10.0%
696
9.6%
5100
10.0%
4108
10.8%
389
8.9%
289
8.9%
1111
11.1%

Tax 5%
Real number (ℝ)

High correlation 

Distinct990
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.400368
Minimum0.5085
Maximum49.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2026-01-19T12:49:10.161100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.5085
5-th percentile1.9575
Q15.89475
median12.096
Q322.5395
95-th percentile39.146
Maximum49.65
Range49.1415
Interquartile range (IQR)16.64475

Descriptive statistics

Standard deviation11.715192
Coefficient of variation (CV)0.76070857
Kurtosis-0.097090352
Mean15.400368
Median Absolute Deviation (MAD)7.518
Skewness0.88698241
Sum15446.569
Variance137.24572
MonotonicityNot monotonic
2026-01-19T12:49:10.298671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.9192
 
0.2%
9.00452
 
0.2%
4.4642
 
0.2%
10.36352
 
0.2%
13.1882
 
0.2%
8.3772
 
0.2%
39.482
 
0.2%
5.8032
 
0.2%
10.3262
 
0.2%
12.572
 
0.2%
Other values (980)983
98.0%
ValueCountFrequency (%)
0.50851
0.1%
0.60451
0.1%
0.6271
0.1%
0.6391
0.1%
0.6991
0.1%
0.7671
0.1%
0.77151
0.1%
0.7751
0.1%
0.8141
0.1%
0.88751
0.1%
ValueCountFrequency (%)
49.651
0.1%
49.491
0.1%
49.261
0.1%
48.751
0.1%
48.691
0.1%
48.6851
0.1%
48.6051
0.1%
47.791
0.1%
47.721
0.1%
45.3251
0.1%

Total
Real number (ℝ)

High correlation 

Distinct990
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean323.40773
Minimum10.6785
Maximum1042.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2026-01-19T12:49:10.432651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10.6785
5-th percentile41.1075
Q1123.78975
median254.016
Q3473.3295
95-th percentile822.066
Maximum1042.65
Range1031.9715
Interquartile range (IQR)349.53975

Descriptive statistics

Standard deviation246.01903
Coefficient of variation (CV)0.76070857
Kurtosis-0.097090352
Mean323.40773
Median Absolute Deviation (MAD)157.878
Skewness0.88698241
Sum324377.95
Variance60525.362
MonotonicityNot monotonic
2026-01-19T12:49:10.566456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
649.2992
 
0.2%
189.09452
 
0.2%
93.7442
 
0.2%
217.63352
 
0.2%
276.9482
 
0.2%
175.9172
 
0.2%
829.082
 
0.2%
121.8632
 
0.2%
216.8462
 
0.2%
263.972
 
0.2%
Other values (980)983
98.0%
ValueCountFrequency (%)
10.67851
0.1%
12.69451
0.1%
13.1671
0.1%
13.4191
0.1%
14.6791
0.1%
16.1071
0.1%
16.20151
0.1%
16.2751
0.1%
17.0941
0.1%
18.63751
0.1%
ValueCountFrequency (%)
1042.651
0.1%
1039.291
0.1%
1034.461
0.1%
1023.751
0.1%
1022.491
0.1%
1022.3851
0.1%
1020.7051
0.1%
1003.591
0.1%
1002.121
0.1%
951.8251
0.1%

Date
Date

Distinct89
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size8.0 KiB
Minimum2019-01-01 00:00:00
Maximum2019-03-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-19T12:49:10.703605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:10.840203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Time
Date

Distinct506
Distinct (%)50.4%
Missing0
Missing (%)0.0%
Memory size8.0 KiB
Minimum2026-01-19 10:00:00
Maximum2026-01-19 20:59:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-19T12:49:10.986175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:11.119011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Payment
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size63.0 KiB
Ewallet
346 
Cash
346 
Credit card
311 

Length

Max length11
Median length7
Mean length7.2053838
Min length4

Characters and Unicode

Total characters7227
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEwallet
2nd rowCash
3rd rowCredit card
4th rowEwallet
5th rowEwallet

Common Values

ValueCountFrequency (%)
Ewallet346
34.5%
Cash346
34.5%
Credit card311
31.0%

Length

2026-01-19T12:49:11.255834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-19T12:49:11.371698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ewallet346
26.3%
cash346
26.3%
credit311
23.7%
card311
23.7%

Most occurring characters

ValueCountFrequency (%)
a1003
13.9%
l692
9.6%
e657
9.1%
t657
9.1%
C657
9.1%
r622
8.6%
d622
8.6%
E346
 
4.8%
w346
 
4.8%
s346
 
4.8%
Other values (4)1279
17.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)7227
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1003
13.9%
l692
9.6%
e657
9.1%
t657
9.1%
C657
9.1%
r622
8.6%
d622
8.6%
E346
 
4.8%
w346
 
4.8%
s346
 
4.8%
Other values (4)1279
17.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7227
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1003
13.9%
l692
9.6%
e657
9.1%
t657
9.1%
C657
9.1%
r622
8.6%
d622
8.6%
E346
 
4.8%
w346
 
4.8%
s346
 
4.8%
Other values (4)1279
17.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7227
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1003
13.9%
l692
9.6%
e657
9.1%
t657
9.1%
C657
9.1%
r622
8.6%
d622
8.6%
E346
 
4.8%
w346
 
4.8%
s346
 
4.8%
Other values (4)1279
17.7%

cogs
Real number (ℝ)

High correlation 

Distinct990
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean308.00736
Minimum10.17
Maximum993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2026-01-19T12:49:11.868575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10.17
5-th percentile39.15
Q1117.895
median241.92
Q3450.79
95-th percentile782.92
Maximum993
Range982.83
Interquartile range (IQR)332.895

Descriptive statistics

Standard deviation234.30384
Coefficient of variation (CV)0.76070857
Kurtosis-0.097090352
Mean308.00736
Median Absolute Deviation (MAD)150.36
Skewness0.88698241
Sum308931.38
Variance54898.288
MonotonicityNot monotonic
2026-01-19T12:49:12.002277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
618.382
 
0.2%
180.092
 
0.2%
89.282
 
0.2%
207.272
 
0.2%
263.762
 
0.2%
167.542
 
0.2%
789.62
 
0.2%
116.062
 
0.2%
206.522
 
0.2%
251.42
 
0.2%
Other values (980)983
98.0%
ValueCountFrequency (%)
10.171
0.1%
12.091
0.1%
12.541
0.1%
12.781
0.1%
13.981
0.1%
15.341
0.1%
15.431
0.1%
15.51
0.1%
16.281
0.1%
17.751
0.1%
ValueCountFrequency (%)
9931
0.1%
989.81
0.1%
985.21
0.1%
9751
0.1%
973.81
0.1%
973.71
0.1%
972.11
0.1%
955.81
0.1%
954.41
0.1%
906.51
0.1%

gross margin percentage
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
4.761904762
1003 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters11033
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.761904762
2nd row4.761904762
3rd row4.761904762
4th row4.761904762
5th row4.761904762

Common Values

ValueCountFrequency (%)
4.7619047621003
100.0%

Length

2026-01-19T12:49:12.118509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-19T12:49:12.216042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4.7619047621003
100.0%

Most occurring characters

ValueCountFrequency (%)
42006
18.2%
72006
18.2%
62006
18.2%
.1003
9.1%
11003
9.1%
91003
9.1%
01003
9.1%
21003
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)11033
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
42006
18.2%
72006
18.2%
62006
18.2%
.1003
9.1%
11003
9.1%
91003
9.1%
01003
9.1%
21003
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11033
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
42006
18.2%
72006
18.2%
62006
18.2%
.1003
9.1%
11003
9.1%
91003
9.1%
01003
9.1%
21003
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11033
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
42006
18.2%
72006
18.2%
62006
18.2%
.1003
9.1%
11003
9.1%
91003
9.1%
01003
9.1%
21003
9.1%

gross income
Real number (ℝ)

High correlation 

Distinct990
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.400368
Minimum0.5085
Maximum49.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2026-01-19T12:49:12.311761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.5085
5-th percentile1.9575
Q15.89475
median12.096
Q322.5395
95-th percentile39.146
Maximum49.65
Range49.1415
Interquartile range (IQR)16.64475

Descriptive statistics

Standard deviation11.715192
Coefficient of variation (CV)0.76070857
Kurtosis-0.097090352
Mean15.400368
Median Absolute Deviation (MAD)7.518
Skewness0.88698241
Sum15446.569
Variance137.24572
MonotonicityNot monotonic
2026-01-19T12:49:12.442674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.9192
 
0.2%
9.00452
 
0.2%
4.4642
 
0.2%
10.36352
 
0.2%
13.1882
 
0.2%
8.3772
 
0.2%
39.482
 
0.2%
5.8032
 
0.2%
10.3262
 
0.2%
12.572
 
0.2%
Other values (980)983
98.0%
ValueCountFrequency (%)
0.50851
0.1%
0.60451
0.1%
0.6271
0.1%
0.6391
0.1%
0.6991
0.1%
0.7671
0.1%
0.77151
0.1%
0.7751
0.1%
0.8141
0.1%
0.88751
0.1%
ValueCountFrequency (%)
49.651
0.1%
49.491
0.1%
49.261
0.1%
48.751
0.1%
48.691
0.1%
48.6851
0.1%
48.6051
0.1%
47.791
0.1%
47.721
0.1%
45.3251
0.1%

Rating
Real number (ℝ)

Distinct61
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.972682
Minimum4
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2026-01-19T12:49:12.573364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4.3
Q15.5
median7
Q38.5
95-th percentile9.7
Maximum10
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7176469
Coefficient of variation (CV)0.24633949
Kurtosis-1.1512945
Mean6.972682
Median Absolute Deviation (MAD)1.5
Skewness0.009592349
Sum6993.6
Variance2.9503109
MonotonicityNot monotonic
2026-01-19T12:49:12.700828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
626
 
2.6%
6.625
 
2.5%
4.222
 
2.2%
9.522
 
2.2%
6.521
 
2.1%
521
 
2.1%
6.221
 
2.1%
821
 
2.1%
5.121
 
2.1%
7.620
 
2.0%
Other values (51)783
78.1%
ValueCountFrequency (%)
411
1.1%
4.117
1.7%
4.222
2.2%
4.318
1.8%
4.417
1.7%
4.517
1.7%
4.68
 
0.8%
4.712
1.2%
4.813
1.3%
4.918
1.8%
ValueCountFrequency (%)
105
 
0.5%
9.916
1.6%
9.819
1.9%
9.714
1.4%
9.617
1.7%
9.522
2.2%
9.412
1.2%
9.316
1.6%
9.216
1.6%
9.114
1.4%

Interactions

2026-01-19T12:49:06.776817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:02.563301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:03.287996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:03.991366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:04.686774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:05.383547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:06.078178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:06.879712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:02.664661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:03.385544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:04.090635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:04.788000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:05.483161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:06.178306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:06.979987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:02.763194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:03.482059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:04.189741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:04.886907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:05.581584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:06.277732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:07.080457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:02.864314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:03.580963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:04.286574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:04.985810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:05.680431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:06.377122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:07.180867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:02.979330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:03.683024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:04.385280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:05.082745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:05.777352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:06.476478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:07.280654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:03.081858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:03.783252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:04.483862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:05.179619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:05.874476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:06.574822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:07.381722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:03.183160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:03.885513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:04.583653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:05.279295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:05.975362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T12:49:06.673061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2026-01-19T12:49:12.817902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
BranchCityCustomer typeGenderPaymentProduct lineQuantityRatingTax 5%TotalUnit pricecogsgross income
Branch1.0001.0000.0000.0400.0000.0120.0190.0000.0000.0000.0000.0000.000
City1.0001.0000.0000.0400.0000.0120.0190.0000.0000.0000.0000.0000.000
Customer type0.0000.0001.0000.0000.0570.0000.0000.0000.0000.0000.0000.0000.000
Gender0.0400.0400.0001.0000.0330.0300.0480.0580.0000.0000.0550.0000.000
Payment0.0000.0000.0570.0331.0000.0000.0000.0000.0000.0000.0310.0000.000
Product line0.0120.0120.0000.0300.0001.0000.0000.0000.0000.0000.0000.0000.000
Quantity0.0190.0190.0000.0480.0000.0001.000-0.0220.7390.7390.0160.7390.739
Rating0.0000.0000.0000.0580.0000.000-0.0221.000-0.020-0.020-0.008-0.020-0.020
Tax 5%0.0000.0000.0000.0000.0000.0000.739-0.0201.0001.0000.6311.0001.000
Total0.0000.0000.0000.0000.0000.0000.739-0.0201.0001.0000.6311.0001.000
Unit price0.0000.0000.0000.0550.0310.0000.016-0.0080.6310.6311.0000.6310.631
cogs0.0000.0000.0000.0000.0000.0000.739-0.0201.0001.0000.6311.0001.000
gross income0.0000.0000.0000.0000.0000.0000.739-0.0201.0001.0000.6311.0001.000

Missing values

2026-01-19T12:49:07.542884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-19T12:49:07.794125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-19T12:49:07.951330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Invoice IDBranchCityCustomer typeGenderProduct lineUnit priceQuantityTax 5%TotalDateTimePaymentcogsgross margin percentagegross incomeRating
0750-67-8428AYangonMemberFemaleHealth and beauty74.697.026.1415548.97151/5/1913:08Ewallet522.834.76190526.14159.1
1226-31-3081CNaypyitawNormalFemaleElectronic accessories15.285.03.820080.22003/8/1910:29Cash76.404.7619053.82009.6
2631-41-3108AYangonNormalMaleHome and lifestyle46.337.016.2155340.52553/3/1913:23Credit card324.314.76190516.21557.4
3123-19-1176AYangonMemberMaleHealth and beauty58.228.023.2880489.04801/27/1920:33Ewallet465.764.76190523.28808.4
4373-73-7910AYangonNormalMaleSports and travel86.317.030.2085634.37852/8/1910:37Ewallet604.174.76190530.20855.3
5699-14-3026CNaypyitawNormalMaleElectronic accessories85.397.029.8865627.61653/25/1918:30Ewallet597.734.76190529.88654.1
6355-53-5943AYangonMemberFemaleNaN68.846.020.6520433.69202/25/1914:36Ewallet413.044.76190520.65205.8
7315-22-5665CNaypyitawNormalFemaleNaN73.5610.036.7800772.38002/24/1911:38Ewallet735.604.76190536.78008.0
8665-32-9167AYangonMemberFemaleNaN36.262.03.626076.14601/10/1917:15Credit card72.524.7619053.62607.2
9692-92-5582BMandalayMemberFemaleNaN54.843.08.2260172.74602/20/1913:27Credit card164.524.7619058.22605.9
Invoice IDBranchCityCustomer typeGenderProduct lineUnit priceQuantityTax 5%TotalDateTimePaymentcogsgross margin percentagegross incomeRating
993690-01-6631BMandalayNormalMaleFashion accessoriesNaN10.08.7450183.64502/22/1918:35Ewallet174.904.7619058.74506.6
994652-49-6720CNaypyitawMemberFemaleElectronic accessoriesNaN1.03.047563.99752/18/1911:40Ewallet60.954.7619053.04755.9
995233-67-5758CNaypyitawNormalMaleHealth and beautyNaN1.02.017542.36751/29/1913:46Ewallet40.354.7619052.01756.2
996303-96-2227BMandalayNormalFemaleHome and lifestyleNaN10.048.69001022.49003/2/1917:16Ewallet973.804.76190548.69004.4
997727-02-1313AYangonMemberMaleFood and beveragesNaN1.01.592033.43202/9/1913:22Cash31.844.7619051.59207.7
998347-56-2442AYangonNormalMaleHome and lifestyle65.821.03.291069.11102/22/1915:33Cash65.824.7619053.29104.1
999849-09-3807AYangonMemberFemaleFashion accessories88.347.030.9190649.29902/18/1913:28Cash618.384.76190530.91906.6
1000849-09-3807AYangonMemberFemaleFashion accessories88.347.030.9190649.29902/18/1913:28Cash618.384.76190530.91906.6
1001745-74-0715AYangonNormalMaleElectronic accessoriesNaN2.05.8030121.86303/10/1920:46Ewallet116.064.7619055.80308.8
1002452-04-8808BMandalayNormalMaleElectronic accessories87.08NaN30.4780640.03801/26/1915:17Cash609.564.76190530.47805.5

Duplicate rows

Most frequently occurring

Invoice IDBranchCityCustomer typeGenderProduct lineUnit priceQuantityTax 5%TotalDateTimePaymentcogsgross margin percentagegross incomeRating# duplicates
0452-04-8808BMandalayNormalMaleElectronic accessories87.08NaN30.478640.0381/26/1915:17Cash609.564.76190530.4785.52
1745-74-0715AYangonNormalMaleElectronic accessoriesNaN2.05.803121.8633/10/1920:46Ewallet116.064.7619055.8038.82
2849-09-3807AYangonMemberFemaleFashion accessories88.347.030.919649.2992/18/1913:28Cash618.384.76190530.9196.62